Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add sammcj/agentic-coding --skill ambitious-model-usegit clone --depth 1 https://github.com/sammcj/agentic-codingWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/sammcj/agentic-coding/ambitious-model-use)<a href="https://agentmods.dev/skills/sammcj/agentic-coding/ambitious-model-use"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/ambitious-model-use/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/sammcj/agentic-coding/ambitious-model-use"><img src="https://agentmods.dev/badge/skills/sammcj/agentic-coding/ambitious-model-use.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00058 | $0.00834 |
| Opus 5.5 | $0.00023 | $0.00334 |
| Sonnet 5.5 | $0.00012 | $0.00167 |
| Haiku 4.5 | $0.00006 | $0.00083 |
Grade A, and why
ambitious-model-use scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Ambitious Model Use - Altitude, Depth and Range
Workflow
- Create a task per step below, each with its completion criterion.
- Diagnose the request on each dimension:
- Altitude: deliverable, or the outcome behind it? Strongest when the work ends in a meeting or decision; weak for open creative work.
- Depth: raw data and the instruments a field-leading expert uses, or digested summaries? Fits any task the user wants done really well.
- Range: only the field the task names on its surface? Strongest for creative work.
- Pick the dimensions with the largest gap and write the rewritten brief using the techniques below. Read
references/examples.mdfirst for worked QBR, churn and homepage rewrites. - List the access the brief needs (a computer, installable tools, raw data via files or MCP connectors) and flag what is missing.
- Name the outside judge: a tool, metric or held-out dataset the model runs. The model's own judgement does not count.
- Present steps 2-5 to the user. Execute the brief only after the user confirms it, applying the Depth and Range techniques yourself.
Altitude Techniques
- Ask the user "what do you want to happen next?" instead of assuming the goal. Only the user can answer it.
- Move from "fix crashes" to "keep the app crash-free". Durable outcomes need workflow changes first: crash-report access, a version-to-commit map, and machines to reproduce and verify fixes.
- Decompose open-ended goals ("go make money") into outcomes the model can verify.
Depth Techniques
Models often change vocabulary without changing methods. Insist on the instruments the best practitioners use.
- Demand databases, statistical models, simulations or domain tools instead of markdown tables.
- Set unreasonable targets and call them reasonable (5 s load time needed -> ask for 200 ms). When the model calls something impossible, restate that it is solvable and have it keep going.
- Adversarially check assumptions against raw data: "We believe X. Try to reproduce that from the raw data."
- Produce new knowledge: build a simulation that reproduces historical data, then find the earliest unknown predictive signal and test it on held-out data.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 49 lines · 58 tokens per session scan A f2e4e6df65df
ambitious-model-use is a skill published in the GitHub repository sammcj/agentic-coding (165 stars, last pushed today), licensed Apache-2.0. It adds 58 tokens to every session and 834 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-19.
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